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<a href="_exact_gradient_8h.html">Go to the documentation of this file.</a><div class="fragment"><div class="line"><a id="l00001" name="l00001"></a><span class="lineno">    1</span><span class="comment">/*!</span></div>
<div class="line"><a id="l00002" name="l00002"></a><span class="lineno">    2</span><span class="comment"> * </span></div>
<div class="line"><a id="l00003" name="l00003"></a><span class="lineno">    3</span><span class="comment"> *</span></div>
<div class="line"><a id="l00004" name="l00004"></a><span class="lineno">    4</span><span class="comment"> * \brief       -</span></div>
<div class="line"><a id="l00005" name="l00005"></a><span class="lineno">    5</span><span class="comment"> *</span></div>
<div class="line"><a id="l00006" name="l00006"></a><span class="lineno">    6</span><span class="comment"> * \author      -</span></div>
<div class="line"><a id="l00007" name="l00007"></a><span class="lineno">    7</span><span class="comment"> * \date        -</span></div>
<div class="line"><a id="l00008" name="l00008"></a><span class="lineno">    8</span><span class="comment"> *</span></div>
<div class="line"><a id="l00009" name="l00009"></a><span class="lineno">    9</span><span class="comment"> *</span></div>
<div class="line"><a id="l00010" name="l00010"></a><span class="lineno">   10</span><span class="comment"> * \par Copyright 1995-2017 Shark Development Team</span></div>
<div class="line"><a id="l00011" name="l00011"></a><span class="lineno">   11</span><span class="comment"> * </span></div>
<div class="line"><a id="l00012" name="l00012"></a><span class="lineno">   12</span><span class="comment"> * &lt;BR&gt;&lt;HR&gt;</span></div>
<div class="line"><a id="l00013" name="l00013"></a><span class="lineno">   13</span><span class="comment"> * This file is part of Shark.</span></div>
<div class="line"><a id="l00014" name="l00014"></a><span class="lineno">   14</span><span class="comment"> * &lt;https://shark-ml.github.io/Shark/&gt;</span></div>
<div class="line"><a id="l00015" name="l00015"></a><span class="lineno">   15</span><span class="comment"> * </span></div>
<div class="line"><a id="l00016" name="l00016"></a><span class="lineno">   16</span><span class="comment"> * Shark is free software: you can redistribute it and/or modify</span></div>
<div class="line"><a id="l00017" name="l00017"></a><span class="lineno">   17</span><span class="comment"> * it under the terms of the GNU Lesser General Public License as published </span></div>
<div class="line"><a id="l00018" name="l00018"></a><span class="lineno">   18</span><span class="comment"> * by the Free Software Foundation, either version 3 of the License, or</span></div>
<div class="line"><a id="l00019" name="l00019"></a><span class="lineno">   19</span><span class="comment"> * (at your option) any later version.</span></div>
<div class="line"><a id="l00020" name="l00020"></a><span class="lineno">   20</span><span class="comment"> * </span></div>
<div class="line"><a id="l00021" name="l00021"></a><span class="lineno">   21</span><span class="comment"> * Shark is distributed in the hope that it will be useful,</span></div>
<div class="line"><a id="l00022" name="l00022"></a><span class="lineno">   22</span><span class="comment"> * but WITHOUT ANY WARRANTY; without even the implied warranty of</span></div>
<div class="line"><a id="l00023" name="l00023"></a><span class="lineno">   23</span><span class="comment"> * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the</span></div>
<div class="line"><a id="l00024" name="l00024"></a><span class="lineno">   24</span><span class="comment"> * GNU Lesser General Public License for more details.</span></div>
<div class="line"><a id="l00025" name="l00025"></a><span class="lineno">   25</span><span class="comment"> * </span></div>
<div class="line"><a id="l00026" name="l00026"></a><span class="lineno">   26</span><span class="comment"> * You should have received a copy of the GNU Lesser General Public License</span></div>
<div class="line"><a id="l00027" name="l00027"></a><span class="lineno">   27</span><span class="comment"> * along with Shark.  If not, see &lt;http://www.gnu.org/licenses/&gt;.</span></div>
<div class="line"><a id="l00028" name="l00028"></a><span class="lineno">   28</span><span class="comment"> *</span></div>
<div class="line"><a id="l00029" name="l00029"></a><span class="lineno">   29</span><span class="comment"> */</span></div>
<div class="line"><a id="l00030" name="l00030"></a><span class="lineno">   30</span><span class="preprocessor">#ifndef SHARK_UNSUPERVISED_RBM_GRADIENTAPPROXIMATIONS_EXACTGRADIENT_H</span></div>
<div class="line"><a id="l00031" name="l00031"></a><span class="lineno">   31</span><span class="preprocessor">#define SHARK_UNSUPERVISED_RBM_GRADIENTAPPROXIMATIONS_EXACTGRADIENT_H</span></div>
<div class="line"><a id="l00032" name="l00032"></a><span class="lineno">   32</span> </div>
<div class="line"><a id="l00033" name="l00033"></a><span class="lineno">   33</span><span class="preprocessor">#include &lt;<a class="code" href="_abstract_objective_function_8h.html" title="AbstractObjectiveFunction.">shark/ObjectiveFunctions/AbstractObjectiveFunction.h</a>&gt;</span></div>
<div class="line"><a id="l00034" name="l00034"></a><span class="lineno">   34</span><span class="preprocessor">#include &lt;<a class="code" href="_gibbs_operator_8h.html">shark/Unsupervised/RBM/Sampling/GibbsOperator.h</a>&gt;</span></div>
<div class="line"><a id="l00035" name="l00035"></a><span class="lineno">   35</span><span class="preprocessor">#include &lt;<a class="code" href="analytics_8h.html">shark/Unsupervised/RBM/analytics.h</a>&gt;</span></div>
<div class="line"><a id="l00036" name="l00036"></a><span class="lineno">   36</span> </div>
<div class="line"><a id="l00037" name="l00037"></a><span class="lineno">   37</span><span class="keyword">namespace </span><a class="code hl_namespace" href="namespaceshark.html" title="AbstractMultiObjectiveOptimizer.">shark</a>{</div>
<div class="line"><a id="l00038" name="l00038"></a><span class="lineno">   38</span> </div>
<div class="line"><a id="l00039" name="l00039"></a><span class="lineno">   39</span><span class="keyword">template</span>&lt;<span class="keyword">class</span> RBMType&gt;</div>
<div class="foldopen" id="foldopen00040" data-start="{" data-end="};">
<div class="line"><a id="l00040" name="l00040"></a><span class="lineno"><a class="line" href="classshark_1_1_exact_gradient.html">   40</a></span><span class="keyword">class </span><a class="code hl_class" href="classshark_1_1_exact_gradient.html">ExactGradient</a>: <span class="keyword">public</span> <a class="code hl_class" href="classshark_1_1_abstract_objective_function.html">SingleObjectiveFunction</a>{</div>
<div class="line"><a id="l00041" name="l00041"></a><span class="lineno">   41</span><span class="keyword">private</span>:</div>
<div class="line"><a id="l00042" name="l00042"></a><span class="lineno">   42</span>    <span class="keyword">typedef</span> <a class="code hl_class" href="classshark_1_1_gibbs_operator.html" title="Implements Block Gibbs Sampling related transition operators for various temperatures.">GibbsOperator&lt;RBMType&gt;</a> <a class="code hl_class" href="classshark_1_1_gibbs_operator.html" title="Implements Block Gibbs Sampling related transition operators for various temperatures.">Gibbs</a>;</div>
<div class="line"><a id="l00043" name="l00043"></a><span class="lineno">   43</span><span class="keyword">public</span>:</div>
<div class="line"><a id="l00044" name="l00044"></a><span class="lineno"><a class="line" href="classshark_1_1_exact_gradient.html#a782d614ec71be0e27fb7fba3bfaa58f4">   44</a></span>    <span class="keyword">typedef</span> RBMType <a class="code hl_typedef" href="classshark_1_1_exact_gradient.html#a782d614ec71be0e27fb7fba3bfaa58f4">RBM</a>;</div>
<div class="line"><a id="l00045" name="l00045"></a><span class="lineno">   45</span> </div>
<div class="foldopen" id="foldopen00046" data-start="{" data-end="}">
<div class="line"><a id="l00046" name="l00046"></a><span class="lineno"><a class="line" href="classshark_1_1_exact_gradient.html#a866e9a502e3c7752eb1fa15e72919275">   46</a></span>    <a class="code hl_function" href="classshark_1_1_exact_gradient.html#a866e9a502e3c7752eb1fa15e72919275">ExactGradient</a>(<a class="code hl_typedef" href="classshark_1_1_exact_gradient.html#a782d614ec71be0e27fb7fba3bfaa58f4">RBM</a>* rbm): mpe_rbm(rbm),m_regularizer(0){</div>
<div class="line"><a id="l00047" name="l00047"></a><span class="lineno">   47</span>        <a class="code hl_define" href="_exception_8h.html#a73abb5049a0168d72a48e72dda41708b">SHARK_ASSERT</a>(rbm != NULL);</div>
<div class="line"><a id="l00048" name="l00048"></a><span class="lineno">   48</span> </div>
<div class="line"><a id="l00049" name="l00049"></a><span class="lineno">   49</span>        <a class="code hl_variable" href="classshark_1_1_abstract_objective_function.html#ad8888c58fd3f98e73013afb5dd4b2af1">m_features</a> |= <a class="code hl_enumvalue" href="classshark_1_1_abstract_objective_function.html#aadafeb6dfb5b649f321e7b81ac8aad1aa0bc7673a369df5f86ddd6ba6735f4971" title="The method evalDerivative is implemented for the first derivative and returns a sensible value.">HAS_FIRST_DERIVATIVE</a>;</div>
<div class="line"><a id="l00050" name="l00050"></a><span class="lineno">   50</span>        <a class="code hl_variable" href="classshark_1_1_abstract_objective_function.html#ad8888c58fd3f98e73013afb5dd4b2af1">m_features</a> |= <a class="code hl_enumvalue" href="classshark_1_1_abstract_objective_function.html#aadafeb6dfb5b649f321e7b81ac8aad1aab9262b57bb302f04b2561666a9068446" title="The function can propose a sensible starting point to search algorithms.">CAN_PROPOSE_STARTING_POINT</a>;</div>
<div class="line"><a id="l00051" name="l00051"></a><span class="lineno">   51</span>    };</div>
</div>
<div class="line"><a id="l00052" name="l00052"></a><span class="lineno">   52</span><span class="comment"></span> </div>
<div class="line"><a id="l00053" name="l00053"></a><span class="lineno">   53</span><span class="comment">    /// \brief From INameable: return the class name.</span></div>
<div class="foldopen" id="foldopen00054" data-start="{" data-end="}">
<div class="line"><a id="l00054" name="l00054"></a><span class="lineno"><a class="line" href="classshark_1_1_exact_gradient.html#a743bdbdd708d3c13c2426ec207f1463b">   54</a></span><span class="comment"></span>    std::string <a class="code hl_function" href="classshark_1_1_exact_gradient.html#a743bdbdd708d3c13c2426ec207f1463b" title="From INameable: return the class name.">name</a>()<span class="keyword"> const</span></div>
<div class="line"><a id="l00055" name="l00055"></a><span class="lineno">   55</span><span class="keyword">    </span>{ <span class="keywordflow">return</span> <span class="stringliteral">&quot;ExactGradient&quot;</span>; }</div>
</div>
<div class="line"><a id="l00056" name="l00056"></a><span class="lineno">   56</span> </div>
<div class="foldopen" id="foldopen00057" data-start="{" data-end="}">
<div class="line"><a id="l00057" name="l00057"></a><span class="lineno"><a class="line" href="classshark_1_1_exact_gradient.html#ad19d6ea347365af7fe15a38e243b1cdf">   57</a></span>    <span class="keywordtype">void</span> <a class="code hl_function" href="classshark_1_1_exact_gradient.html#ad19d6ea347365af7fe15a38e243b1cdf">setData</a>(<a class="code hl_class" href="classshark_1_1_unlabeled_data.html" title="Data set for unsupervised learning.">UnlabeledData&lt;RealVector&gt;</a> <span class="keyword">const</span>&amp; data){</div>
<div class="line"><a id="l00058" name="l00058"></a><span class="lineno">   58</span>        m_data = data;</div>
<div class="line"><a id="l00059" name="l00059"></a><span class="lineno">   59</span>    }</div>
</div>
<div class="line"><a id="l00060" name="l00060"></a><span class="lineno">   60</span> </div>
<div class="foldopen" id="foldopen00061" data-start="{" data-end="}">
<div class="line"><a id="l00061" name="l00061"></a><span class="lineno"><a class="line" href="classshark_1_1_exact_gradient.html#a4ca798c77e7b1cf431b8459b1358b8d5">   61</a></span>    <a class="code hl_typedef" href="classshark_1_1_abstract_objective_function.html#a59bfea031628e16737c66e7117eba7b5">SearchPointType</a> <a class="code hl_function" href="classshark_1_1_exact_gradient.html#a4ca798c77e7b1cf431b8459b1358b8d5" title="Proposes a starting point in the feasible search space of the function.">proposeStartingPoint</a>()<span class="keyword"> const</span>{</div>
<div class="line"><a id="l00062" name="l00062"></a><span class="lineno">   62</span>        <span class="keywordflow">return</span>  mpe_rbm-&gt;parameterVector();</div>
<div class="line"><a id="l00063" name="l00063"></a><span class="lineno">   63</span>    }</div>
</div>
<div class="line"><a id="l00064" name="l00064"></a><span class="lineno">   64</span>    </div>
<div class="foldopen" id="foldopen00065" data-start="{" data-end="}">
<div class="line"><a id="l00065" name="l00065"></a><span class="lineno"><a class="line" href="classshark_1_1_exact_gradient.html#a4b11a51852eebba4972658b9bf41d26c">   65</a></span>    std::size_t <a class="code hl_function" href="classshark_1_1_exact_gradient.html#a4b11a51852eebba4972658b9bf41d26c" title="Accesses the number of variables.">numberOfVariables</a>()<span class="keyword">const</span>{</div>
<div class="line"><a id="l00066" name="l00066"></a><span class="lineno">   66</span>        <span class="keywordflow">return</span> mpe_rbm-&gt;numberOfParameters();</div>
<div class="line"><a id="l00067" name="l00067"></a><span class="lineno">   67</span>    }</div>
</div>
<div class="line"><a id="l00068" name="l00068"></a><span class="lineno">   68</span>    </div>
<div class="foldopen" id="foldopen00069" data-start="{" data-end="}">
<div class="line"><a id="l00069" name="l00069"></a><span class="lineno"><a class="line" href="classshark_1_1_exact_gradient.html#a3eb3523455c880a8de64e9459f29c038">   69</a></span>    <span class="keywordtype">void</span> <a class="code hl_function" href="classshark_1_1_exact_gradient.html#a3eb3523455c880a8de64e9459f29c038">setRegularizer</a>(<span class="keywordtype">double</span> factor, <a class="code hl_class" href="classshark_1_1_abstract_objective_function.html">SingleObjectiveFunction</a>* regularizer){</div>
<div class="line"><a id="l00070" name="l00070"></a><span class="lineno">   70</span>        m_regularizer = regularizer;</div>
<div class="line"><a id="l00071" name="l00071"></a><span class="lineno">   71</span>        m_regularizationStrength = factor;</div>
<div class="line"><a id="l00072" name="l00072"></a><span class="lineno">   72</span>    }</div>
</div>
<div class="line"><a id="l00073" name="l00073"></a><span class="lineno">   73</span>    </div>
<div class="foldopen" id="foldopen00074" data-start="{" data-end="}">
<div class="line"><a id="l00074" name="l00074"></a><span class="lineno"><a class="line" href="classshark_1_1_exact_gradient.html#ac633cfe3ffabe6a136ed64243e18dd66">   74</a></span>    <span class="keywordtype">double</span> <a class="code hl_function" href="classshark_1_1_exact_gradient.html#ac633cfe3ffabe6a136ed64243e18dd66" title="Evaluates the objective function for the supplied argument.">eval</a>( <a class="code hl_typedef" href="classshark_1_1_abstract_objective_function.html#a59bfea031628e16737c66e7117eba7b5">SearchPointType</a> <span class="keyword">const</span> &amp; parameter)<span class="keyword"> const </span>{</div>
<div class="line"><a id="l00075" name="l00075"></a><span class="lineno">   75</span>        mpe_rbm-&gt;setParameterVector(parameter);</div>
<div class="line"><a id="l00076" name="l00076"></a><span class="lineno">   76</span>        </div>
<div class="line"><a id="l00077" name="l00077"></a><span class="lineno">   77</span>        <span class="keywordtype">double</span> negLogLikelihood = <a class="code hl_function" href="namespaceshark.html#a99371d021e025a4f27232feb1246d4e7" title="Estimates the negative log-likelihood of a set of input vectors under the models distribution.">negativeLogLikelihood</a>(*mpe_rbm,m_data)/m_data.<a class="code hl_function" href="group__shark__globals.html#ga814e8b0028cc90dd2af69805e8f8a04d" title="Returns the total number of elements.">numberOfElements</a>();</div>
<div class="line"><a id="l00078" name="l00078"></a><span class="lineno">   78</span>        <span class="keywordflow">if</span>(m_regularizer){</div>
<div class="line"><a id="l00079" name="l00079"></a><span class="lineno">   79</span>            negLogLikelihood += m_regularizationStrength * m_regularizer-&gt;<a class="code hl_function" href="classshark_1_1_abstract_objective_function.html#a751c175270f6d6f0bcc1200f333c0045" title="Evaluates the objective function for the supplied argument.">eval</a>(parameter);</div>
<div class="line"><a id="l00080" name="l00080"></a><span class="lineno">   80</span>        }</div>
<div class="line"><a id="l00081" name="l00081"></a><span class="lineno">   81</span>        <span class="keywordflow">return</span> negLogLikelihood;</div>
<div class="line"><a id="l00082" name="l00082"></a><span class="lineno">   82</span>    }</div>
</div>
<div class="line"><a id="l00083" name="l00083"></a><span class="lineno">   83</span> </div>
<div class="foldopen" id="foldopen00084" data-start="{" data-end="}">
<div class="line"><a id="l00084" name="l00084"></a><span class="lineno"><a class="line" href="classshark_1_1_exact_gradient.html#aef523213c55d8c9c6261abfe3c4c6bcf">   84</a></span>    <span class="keywordtype">double</span> <a class="code hl_function" href="classshark_1_1_exact_gradient.html#aef523213c55d8c9c6261abfe3c4c6bcf" title="Evaluates the objective function and calculates its gradient.">evalDerivative</a>( <a class="code hl_typedef" href="classshark_1_1_abstract_objective_function.html#a59bfea031628e16737c66e7117eba7b5">SearchPointType</a> <span class="keyword">const</span> &amp; parameter, <a class="code hl_typedef" href="classshark_1_1_abstract_objective_function.html#a29804371954a360f09696adea7cfd839">FirstOrderDerivative</a> &amp; derivative )<span class="keyword"> const </span>{</div>
<div class="line"><a id="l00085" name="l00085"></a><span class="lineno">   85</span>        mpe_rbm-&gt;setParameterVector(parameter);</div>
<div class="line"><a id="l00086" name="l00086"></a><span class="lineno">   86</span>        </div>
<div class="line"><a id="l00087" name="l00087"></a><span class="lineno">   87</span>        <span class="comment">//the gradient approximation for the energy terms of the RBM        </span></div>
<div class="line"><a id="l00088" name="l00088"></a><span class="lineno">   88</span>        <span class="keyword">typename</span> <a class="code hl_typedef" href="classshark_1_1_r_b_m.html#a916086702525de4b9ccd1a715f4317d8" title="Type of the gradient calculator.">RBM::GradientType</a> empiricalExpectation(mpe_rbm);</div>
<div class="line"><a id="l00089" name="l00089"></a><span class="lineno">   89</span>        <span class="keyword">typename</span> <a class="code hl_typedef" href="classshark_1_1_r_b_m.html#a916086702525de4b9ccd1a715f4317d8" title="Type of the gradient calculator.">RBM::GradientType</a> modelExpectation(mpe_rbm);</div>
<div class="line"><a id="l00090" name="l00090"></a><span class="lineno">   90</span> </div>
<div class="line"><a id="l00091" name="l00091"></a><span class="lineno">   91</span>        <a class="code hl_class" href="classshark_1_1_gibbs_operator.html" title="Implements Block Gibbs Sampling related transition operators for various temperatures.">Gibbs</a> gibbsSampler(mpe_rbm);</div>
<div class="line"><a id="l00092" name="l00092"></a><span class="lineno">   92</span>        </div>
<div class="line"><a id="l00093" name="l00093"></a><span class="lineno">   93</span>        <span class="comment">//calculate the expectation of the energy gradient with respect to the data</span></div>
<div class="line"><a id="l00094" name="l00094"></a><span class="lineno">   94</span>        <span class="keywordtype">double</span> negLogLikelihood = 0;</div>
<div class="line"><a id="l00095" name="l00095"></a><span class="lineno">   95</span>        <span class="keywordflow">for</span>(RealMatrix <span class="keyword">const</span>&amp; batch: m_data.<a class="code hl_function" href="group__shark__globals.html#ga4edf9849713708253a4d1f2d31e6187b" title="Returns the range of batches.">batches</a>()) {</div>
<div class="line"><a id="l00096" name="l00096"></a><span class="lineno">   96</span>            std::size_t currentBatchSize = batch.size1();</div>
<div class="line"><a id="l00097" name="l00097"></a><span class="lineno">   97</span>            <span class="keyword">typename</span> <a class="code hl_typedef" href="classshark_1_1_gibbs_operator.html#a5b57c0bacafe33d8d3ed614d4dd5d6dd" title="Represents the state of a batch of hidden samples and additional information required by the gradient...">Gibbs::HiddenSampleBatch</a> hiddenSamples(currentBatchSize,mpe_rbm-&gt;numberOfHN());</div>
<div class="line"><a id="l00098" name="l00098"></a><span class="lineno">   98</span>            <span class="keyword">typename</span> <a class="code hl_typedef" href="classshark_1_1_gibbs_operator.html#a0c5a9c2d399cebdb3e036a5803a1d28b" title="Represents the state of the visible units and additional information required by the gradient.">Gibbs::VisibleSampleBatch</a> visibleSamples(currentBatchSize,mpe_rbm-&gt;numberOfVN());</div>
<div class="line"><a id="l00099" name="l00099"></a><span class="lineno">   99</span>        </div>
<div class="line"><a id="l00100" name="l00100"></a><span class="lineno">  100</span>            gibbsSampler.<a class="code hl_function" href="classshark_1_1_gibbs_operator.html#a6cf9658a4eb72ac3dc3dbaa44a51c726" title="Creates hidden/visible sample pairs from the states of the visible neurons, i.e. sets the visible uni...">createSample</a>(hiddenSamples,visibleSamples,batch);</div>
<div class="line"><a id="l00101" name="l00101"></a><span class="lineno">  101</span>            empiricalExpectation.addVH(hiddenSamples, visibleSamples);</div>
<div class="line"><a id="l00102" name="l00102"></a><span class="lineno">  102</span>            negLogLikelihood -= sum(mpe_rbm-&gt;energy().logUnnormalizedProbabilityVisible(</div>
<div class="line"><a id="l00103" name="l00103"></a><span class="lineno">  103</span>                batch,hiddenSamples.input,blas::repeat(1,currentBatchSize)</div>
<div class="line"><a id="l00104" name="l00104"></a><span class="lineno">  104</span>            ));</div>
<div class="line"><a id="l00105" name="l00105"></a><span class="lineno">  105</span>        }</div>
<div class="line"><a id="l00106" name="l00106"></a><span class="lineno">  106</span>        </div>
<div class="line"><a id="l00107" name="l00107"></a><span class="lineno">  107</span>        <span class="comment">//calculate the expectation of the energy gradient with respect to the model distribution</span></div>
<div class="line"><a id="l00108" name="l00108"></a><span class="lineno">  108</span>        <span class="keywordflow">if</span>(mpe_rbm-&gt;numberOfVN() &lt; mpe_rbm-&gt;numberOfHN()){</div>
<div class="line"><a id="l00109" name="l00109"></a><span class="lineno">  109</span>            integrateOverVisible(modelExpectation);</div>
<div class="line"><a id="l00110" name="l00110"></a><span class="lineno">  110</span>        }</div>
<div class="line"><a id="l00111" name="l00111"></a><span class="lineno">  111</span>        <span class="keywordflow">else</span>{</div>
<div class="line"><a id="l00112" name="l00112"></a><span class="lineno">  112</span>            integrateOverHidden(modelExpectation);</div>
<div class="line"><a id="l00113" name="l00113"></a><span class="lineno">  113</span>        }</div>
<div class="line"><a id="l00114" name="l00114"></a><span class="lineno">  114</span>        </div>
<div class="line"><a id="l00115" name="l00115"></a><span class="lineno">  115</span>        derivative.resize(mpe_rbm-&gt;numberOfParameters());</div>
<div class="line"><a id="l00116" name="l00116"></a><span class="lineno">  116</span>        noalias(derivative) = modelExpectation.result() - empiricalExpectation.result();</div>
<div class="line"><a id="l00117" name="l00117"></a><span class="lineno">  117</span>    </div>
<div class="line"><a id="l00118" name="l00118"></a><span class="lineno">  118</span>        m_logPartition = modelExpectation.logWeightSum();</div>
<div class="line"><a id="l00119" name="l00119"></a><span class="lineno">  119</span>        negLogLikelihood/=m_data.<a class="code hl_function" href="group__shark__globals.html#ga814e8b0028cc90dd2af69805e8f8a04d" title="Returns the total number of elements.">numberOfElements</a>();</div>
<div class="line"><a id="l00120" name="l00120"></a><span class="lineno">  120</span>        negLogLikelihood += m_logPartition;</div>
<div class="line"><a id="l00121" name="l00121"></a><span class="lineno">  121</span>        </div>
<div class="line"><a id="l00122" name="l00122"></a><span class="lineno">  122</span>        <span class="keywordflow">if</span>(m_regularizer){</div>
<div class="line"><a id="l00123" name="l00123"></a><span class="lineno">  123</span>            <a class="code hl_typedef" href="classshark_1_1_abstract_objective_function.html#a29804371954a360f09696adea7cfd839">FirstOrderDerivative</a> regularizerDerivative;</div>
<div class="line"><a id="l00124" name="l00124"></a><span class="lineno">  124</span>            negLogLikelihood += m_regularizationStrength * m_regularizer-&gt;<a class="code hl_function" href="classshark_1_1_abstract_objective_function.html#a53df2ac5d82c608ea938dc1e3a0c0617" title="Evaluates the objective function and calculates its gradient.">evalDerivative</a>(parameter,regularizerDerivative);</div>
<div class="line"><a id="l00125" name="l00125"></a><span class="lineno">  125</span>            noalias(derivative) += m_regularizationStrength * regularizerDerivative;</div>
<div class="line"><a id="l00126" name="l00126"></a><span class="lineno">  126</span>        }</div>
<div class="line"><a id="l00127" name="l00127"></a><span class="lineno">  127</span>        </div>
<div class="line"><a id="l00128" name="l00128"></a><span class="lineno">  128</span>        <span class="keywordflow">return</span> negLogLikelihood;</div>
<div class="line"><a id="l00129" name="l00129"></a><span class="lineno">  129</span>    }</div>
</div>
<div class="line"><a id="l00130" name="l00130"></a><span class="lineno">  130</span> </div>
<div class="foldopen" id="foldopen00131" data-start="{" data-end="}">
<div class="line"><a id="l00131" name="l00131"></a><span class="lineno"><a class="line" href="classshark_1_1_exact_gradient.html#aebc03b4ccfbb70f873c3a932cebe9ddd">  131</a></span>    <span class="keywordtype">long</span> <span class="keywordtype">double</span> <a class="code hl_function" href="classshark_1_1_exact_gradient.html#aebc03b4ccfbb70f873c3a932cebe9ddd">getLogPartition</a>(){</div>
<div class="line"><a id="l00132" name="l00132"></a><span class="lineno">  132</span>        <span class="keywordflow">return</span> m_logPartition;</div>
<div class="line"><a id="l00133" name="l00133"></a><span class="lineno">  133</span>    }</div>
</div>
<div class="line"><a id="l00134" name="l00134"></a><span class="lineno">  134</span> </div>
<div class="line"><a id="l00135" name="l00135"></a><span class="lineno">  135</span><span class="keyword">private</span>:</div>
<div class="line"><a id="l00136" name="l00136"></a><span class="lineno">  136</span>    <a class="code hl_class" href="classshark_1_1_r_b_m.html" title="stub for the RBM class. at the moment it is just a holder of the parameter set and the Energy.">RBM</a>* mpe_rbm;</div>
<div class="line"><a id="l00137" name="l00137"></a><span class="lineno">  137</span> </div>
<div class="line"><a id="l00138" name="l00138"></a><span class="lineno">  138</span>    <a class="code hl_class" href="classshark_1_1_abstract_objective_function.html">SingleObjectiveFunction</a>* m_regularizer;</div>
<div class="line"><a id="l00139" name="l00139"></a><span class="lineno">  139</span>    <span class="keywordtype">double</span> m_regularizationStrength;</div>
<div class="line"><a id="l00140" name="l00140"></a><span class="lineno">  140</span>    </div>
<div class="line"><a id="l00141" name="l00141"></a><span class="lineno">  141</span>    <span class="comment">//batchwise loops over all hidden units to calculate the gradient as well as partition</span></div>
<div class="line"><a id="l00142" name="l00142"></a><span class="lineno">  142</span>    <span class="keyword">template</span>&lt;<span class="keyword">class</span> GradientApproximator&gt;<span class="comment">//mostly dummy right now</span></div>
<div class="line"><a id="l00143" name="l00143"></a><span class="lineno">  143</span>    <span class="keywordtype">void</span> integrateOverVisible(GradientApproximator &amp; modelExpectation)<span class="keyword"> const</span>{</div>
<div class="line"><a id="l00144" name="l00144"></a><span class="lineno">  144</span>        </div>
<div class="line"><a id="l00145" name="l00145"></a><span class="lineno">  145</span>        Gibbs sampler(mpe_rbm);</div>
<div class="line"><a id="l00146" name="l00146"></a><span class="lineno">  146</span>        </div>
<div class="line"><a id="l00147" name="l00147"></a><span class="lineno">  147</span>        <span class="keyword">typedef</span> <span class="keyword">typename</span> RBM::VisibleType::StateSpace VisibleStateSpace;</div>
<div class="line"><a id="l00148" name="l00148"></a><span class="lineno">  148</span>        std::size_t values = VisibleStateSpace::numberOfStates(mpe_rbm-&gt;<a class="code hl_function" href="classshark_1_1_r_b_m.html#aa2832c9073247890ae6f17285cc5056c" title="Returns the number of visible Neurons.">numberOfVN</a>());</div>
<div class="line"><a id="l00149" name="l00149"></a><span class="lineno">  149</span>        std::size_t <a class="code hl_function" href="namespaceshark.html#af2ab10364feb8a631e0866dcf2f1a4ad">batchSize</a> = std::min(values, std::size_t(256));</div>
<div class="line"><a id="l00150" name="l00150"></a><span class="lineno">  150</span>        </div>
<div class="line"><a id="l00151" name="l00151"></a><span class="lineno">  151</span>        <span class="keywordflow">for</span> (std::size_t x = 0; x &lt; values; x+=<a class="code hl_function" href="namespaceshark.html#af2ab10364feb8a631e0866dcf2f1a4ad">batchSize</a>) {</div>
<div class="line"><a id="l00152" name="l00152"></a><span class="lineno">  152</span>            <span class="comment">//create batch of states</span></div>
<div class="line"><a id="l00153" name="l00153"></a><span class="lineno">  153</span>            std::size_t currentBatchSize=std::min(<a class="code hl_function" href="namespaceshark.html#af2ab10364feb8a631e0866dcf2f1a4ad">batchSize</a>,values-x);</div>
<div class="line"><a id="l00154" name="l00154"></a><span class="lineno">  154</span>            <span class="keyword">typename</span> <a class="code hl_struct" href="structshark_1_1_batch.html" title="class which helps using different batch types">Batch&lt;RealVector&gt;::type</a> stateBatch(currentBatchSize,mpe_rbm-&gt;<a class="code hl_function" href="classshark_1_1_r_b_m.html#aa2832c9073247890ae6f17285cc5056c" title="Returns the number of visible Neurons.">numberOfVN</a>());</div>
<div class="line"><a id="l00155" name="l00155"></a><span class="lineno">  155</span>            <span class="keywordflow">for</span>(std::size_t elem = 0; elem != currentBatchSize;++elem){</div>
<div class="line"><a id="l00156" name="l00156"></a><span class="lineno">  156</span>                <span class="comment">//generation of the x+elem-th state vector</span></div>
<div class="line"><a id="l00157" name="l00157"></a><span class="lineno">  157</span>                VisibleStateSpace::state(row(stateBatch,elem),x+elem);</div>
<div class="line"><a id="l00158" name="l00158"></a><span class="lineno">  158</span>            }</div>
<div class="line"><a id="l00159" name="l00159"></a><span class="lineno">  159</span>            </div>
<div class="line"><a id="l00160" name="l00160"></a><span class="lineno">  160</span>            <span class="comment">//create sample from state batch</span></div>
<div class="line"><a id="l00161" name="l00161"></a><span class="lineno">  161</span>            <span class="keyword">typename</span> <a class="code hl_typedef" href="classshark_1_1_gibbs_operator.html#a5b57c0bacafe33d8d3ed614d4dd5d6dd" title="Represents the state of a batch of hidden samples and additional information required by the gradient...">Gibbs::HiddenSampleBatch</a> hiddenBatch(currentBatchSize,mpe_rbm-&gt;<a class="code hl_function" href="classshark_1_1_r_b_m.html#aff68280f2b354df64b4ac311bcd0a240" title="Returns the number of hidden Neurons.">numberOfHN</a>());</div>
<div class="line"><a id="l00162" name="l00162"></a><span class="lineno">  162</span>            <span class="keyword">typename</span> <a class="code hl_typedef" href="classshark_1_1_gibbs_operator.html#a0c5a9c2d399cebdb3e036a5803a1d28b" title="Represents the state of the visible units and additional information required by the gradient.">Gibbs::VisibleSampleBatch</a> visibleBatch(currentBatchSize,mpe_rbm-&gt;<a class="code hl_function" href="classshark_1_1_r_b_m.html#aa2832c9073247890ae6f17285cc5056c" title="Returns the number of visible Neurons.">numberOfVN</a>());</div>
<div class="line"><a id="l00163" name="l00163"></a><span class="lineno">  163</span>            sampler.createSample(hiddenBatch,visibleBatch,stateBatch);</div>
<div class="line"><a id="l00164" name="l00164"></a><span class="lineno">  164</span>            </div>
<div class="line"><a id="l00165" name="l00165"></a><span class="lineno">  165</span>            <span class="comment">//calculate probabilities and update </span></div>
<div class="line"><a id="l00166" name="l00166"></a><span class="lineno">  166</span>            RealVector logP = mpe_rbm-&gt;<a class="code hl_function" href="classshark_1_1_r_b_m.html#a8fb50f496bfd20e8a3e1cd9573b82ce2" title="Returns the energy function of the RBM.">energy</a>().<a class="code hl_function" href="structshark_1_1_energy.html#a7d21da0cfe60fabb495a5fa0d75cce51" title="Computes the logarithm of the unnormalized probability of each state of the visible neurons in a batc...">logUnnormalizedProbabilityVisible</a>(</div>
<div class="line"><a id="l00167" name="l00167"></a><span class="lineno">  167</span>                stateBatch,hiddenBatch.input,blas::repeat(1,currentBatchSize)</div>
<div class="line"><a id="l00168" name="l00168"></a><span class="lineno">  168</span>            );</div>
<div class="line"><a id="l00169" name="l00169"></a><span class="lineno">  169</span>            modelExpectation.addVH(hiddenBatch, visibleBatch, logP);</div>
<div class="line"><a id="l00170" name="l00170"></a><span class="lineno">  170</span>        }</div>
<div class="line"><a id="l00171" name="l00171"></a><span class="lineno">  171</span>    }</div>
<div class="line"><a id="l00172" name="l00172"></a><span class="lineno">  172</span>    </div>
<div class="line"><a id="l00173" name="l00173"></a><span class="lineno">  173</span>    <span class="comment">//batchwise loops over all hidden units to calculate the gradient as well as partition</span></div>
<div class="line"><a id="l00174" name="l00174"></a><span class="lineno">  174</span>    <span class="keyword">template</span>&lt;<span class="keyword">class</span> GradientApproximator&gt;<span class="comment">//mostly dummy right now</span></div>
<div class="line"><a id="l00175" name="l00175"></a><span class="lineno">  175</span>    <span class="keywordtype">void</span> integrateOverHidden(GradientApproximator &amp; modelExpectation)<span class="keyword"> const</span>{</div>
<div class="line"><a id="l00176" name="l00176"></a><span class="lineno">  176</span>        </div>
<div class="line"><a id="l00177" name="l00177"></a><span class="lineno">  177</span>        Gibbs sampler(mpe_rbm);</div>
<div class="line"><a id="l00178" name="l00178"></a><span class="lineno">  178</span>        </div>
<div class="line"><a id="l00179" name="l00179"></a><span class="lineno">  179</span>        <span class="keyword">typedef</span> <span class="keyword">typename</span> RBM::HiddenType::StateSpace HiddenStateSpace;</div>
<div class="line"><a id="l00180" name="l00180"></a><span class="lineno">  180</span>        std::size_t values = HiddenStateSpace::numberOfStates(mpe_rbm-&gt;<a class="code hl_function" href="classshark_1_1_r_b_m.html#aff68280f2b354df64b4ac311bcd0a240" title="Returns the number of hidden Neurons.">numberOfHN</a>());</div>
<div class="line"><a id="l00181" name="l00181"></a><span class="lineno">  181</span>        std::size_t <a class="code hl_function" href="namespaceshark.html#af2ab10364feb8a631e0866dcf2f1a4ad">batchSize</a> = std::min(values, std::size_t(256) );</div>
<div class="line"><a id="l00182" name="l00182"></a><span class="lineno">  182</span>        </div>
<div class="line"><a id="l00183" name="l00183"></a><span class="lineno">  183</span>        <span class="keywordflow">for</span> (std::size_t x = 0; x &lt; values; x+=<a class="code hl_function" href="namespaceshark.html#af2ab10364feb8a631e0866dcf2f1a4ad">batchSize</a>) {</div>
<div class="line"><a id="l00184" name="l00184"></a><span class="lineno">  184</span>            <span class="comment">//create batch of states</span></div>
<div class="line"><a id="l00185" name="l00185"></a><span class="lineno">  185</span>            std::size_t currentBatchSize=std::min(<a class="code hl_function" href="namespaceshark.html#af2ab10364feb8a631e0866dcf2f1a4ad">batchSize</a>,values-x);</div>
<div class="line"><a id="l00186" name="l00186"></a><span class="lineno">  186</span>            <span class="keyword">typename</span> Batch&lt;RealVector&gt;::type stateBatch(currentBatchSize,mpe_rbm-&gt;<a class="code hl_function" href="classshark_1_1_r_b_m.html#aff68280f2b354df64b4ac311bcd0a240" title="Returns the number of hidden Neurons.">numberOfHN</a>());</div>
<div class="line"><a id="l00187" name="l00187"></a><span class="lineno">  187</span>            <span class="keywordflow">for</span>(std::size_t elem = 0; elem != currentBatchSize;++elem){</div>
<div class="line"><a id="l00188" name="l00188"></a><span class="lineno">  188</span>                <span class="comment">//generation of the x+elem-th state vector</span></div>
<div class="line"><a id="l00189" name="l00189"></a><span class="lineno">  189</span>                HiddenStateSpace::state(row(stateBatch,elem),x+elem);</div>
<div class="line"><a id="l00190" name="l00190"></a><span class="lineno">  190</span>            }</div>
<div class="line"><a id="l00191" name="l00191"></a><span class="lineno">  191</span>            </div>
<div class="line"><a id="l00192" name="l00192"></a><span class="lineno">  192</span>            <span class="comment">//create sample from state batch</span></div>
<div class="line"><a id="l00193" name="l00193"></a><span class="lineno">  193</span>            <span class="keyword">typename</span> <a class="code hl_typedef" href="classshark_1_1_gibbs_operator.html#a5b57c0bacafe33d8d3ed614d4dd5d6dd" title="Represents the state of a batch of hidden samples and additional information required by the gradient...">Gibbs::HiddenSampleBatch</a> hiddenBatch(currentBatchSize,mpe_rbm-&gt;<a class="code hl_function" href="classshark_1_1_r_b_m.html#aff68280f2b354df64b4ac311bcd0a240" title="Returns the number of hidden Neurons.">numberOfHN</a>());</div>
<div class="line"><a id="l00194" name="l00194"></a><span class="lineno">  194</span>            <span class="keyword">typename</span> <a class="code hl_typedef" href="classshark_1_1_gibbs_operator.html#a0c5a9c2d399cebdb3e036a5803a1d28b" title="Represents the state of the visible units and additional information required by the gradient.">Gibbs::VisibleSampleBatch</a> visibleBatch(currentBatchSize,mpe_rbm-&gt;<a class="code hl_function" href="classshark_1_1_r_b_m.html#aa2832c9073247890ae6f17285cc5056c" title="Returns the number of visible Neurons.">numberOfVN</a>());</div>
<div class="line"><a id="l00195" name="l00195"></a><span class="lineno">  195</span>            hiddenBatch.state=stateBatch;</div>
<div class="line"><a id="l00196" name="l00196"></a><span class="lineno">  196</span>            sampler.precomputeVisible(hiddenBatch,visibleBatch, blas::repeat(1,currentBatchSize));</div>
<div class="line"><a id="l00197" name="l00197"></a><span class="lineno">  197</span>            </div>
<div class="line"><a id="l00198" name="l00198"></a><span class="lineno">  198</span>            <span class="comment">//calculate probabilities and update </span></div>
<div class="line"><a id="l00199" name="l00199"></a><span class="lineno">  199</span>            RealVector logP = mpe_rbm-&gt;<a class="code hl_function" href="classshark_1_1_r_b_m.html#a8fb50f496bfd20e8a3e1cd9573b82ce2" title="Returns the energy function of the RBM.">energy</a>().<a class="code hl_function" href="structshark_1_1_energy.html#afa5365f469f52d0682d3820fb071fa1b" title="Computes the logarithm of the unnormalized probability of each state of the hidden neurons in a batch...">logUnnormalizedProbabilityHidden</a>(</div>
<div class="line"><a id="l00200" name="l00200"></a><span class="lineno">  200</span>                stateBatch,visibleBatch.input,blas::repeat(1,currentBatchSize)</div>
<div class="line"><a id="l00201" name="l00201"></a><span class="lineno">  201</span>            );</div>
<div class="line"><a id="l00202" name="l00202"></a><span class="lineno">  202</span>            modelExpectation.addHV(hiddenBatch, visibleBatch, logP);</div>
<div class="line"><a id="l00203" name="l00203"></a><span class="lineno">  203</span>        }</div>
<div class="line"><a id="l00204" name="l00204"></a><span class="lineno">  204</span>    }</div>
<div class="line"><a id="l00205" name="l00205"></a><span class="lineno">  205</span> </div>
<div class="line"><a id="l00206" name="l00206"></a><span class="lineno">  206</span>    UnlabeledData&lt;RealVector&gt; m_data;</div>
<div class="line"><a id="l00207" name="l00207"></a><span class="lineno">  207</span> </div>
<div class="line"><a id="l00208" name="l00208"></a><span class="lineno">  208</span>    <span class="keyword">mutable</span> <span class="keywordtype">double</span> m_logPartition; <span class="comment">//the partition function of the model distribution</span></div>
<div class="line"><a id="l00209" name="l00209"></a><span class="lineno">  209</span>};  </div>
</div>
<div class="line"><a id="l00210" name="l00210"></a><span class="lineno">  210</span>    </div>
<div class="line"><a id="l00211" name="l00211"></a><span class="lineno">  211</span>}</div>
<div class="line"><a id="l00212" name="l00212"></a><span class="lineno">  212</span> </div>
<div class="line"><a id="l00213" name="l00213"></a><span class="lineno">  213</span><span class="preprocessor">#endif</span></div>
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